Assessment of Physical-Chemical Characteristics of Water and Sediments from a Brazilian Tropical Estuary: Status and Environmental Implications

The environmental quality of the Jacuípe River's estuary (very important in northeastern Brazil) was assessed during 2007 and 2008. In water, concentrations (mg L−1) of NO2− (<0.004 to 0.016), NO3− (0.01 to 0.33), soluble PO43− (<0.02 to 0.22), dissolved oxygen (3.9 to 9.6), total contents (mg L−1) of Cd (<0.001), Cu (<0.01), Pb (<0.01), and Zn (<0.1), pH (5.60 to 8.00), and electrical conductivity (0.12 to 48.60 mS cm−1) agreed with environmental standards. In sediments, clay and total organic matter (%, m/m) varied, respectively, from 8.8 to 12.0 and from 1.1 to 8.8, while infrared, thermogravimetric profile, electronic micrograph, as well as X-Ray analyses showed desirable adsorptive characteristics. However, maximum exchangeable levels (mg kg−1) of Cd (1.3), Cu (44.6), Pb (35.7), and Zn (43.7) and their respective maximum pseudototal concentrations (mg kg−1): 19.4, 95.1, 68.2, and 30.3 were below the recommended limits. In this sense, it was possible to demonstrate good environmental preservation even with the growing number of industries and touristic activities in the evaluated estuarine area

XAF1 as a modifier of p53 function and cancer susceptibility

Cancer risk is highly variable in carriers of the common TP53-R337H founder allele, possibly due to the influence of modifier genes. Whole-genome sequencing identified a variant in the tumor suppressor XAF1 (E134*/Glu134Ter/rs146752602) in a subset of R337H carriers. Haplotype-defining variants were verified in 203 patients with cancer, 582 relatives, and 42,438 newborns. The compound mutant haplotype was enriched in patients with cancer, conferring risk for sarcoma (P = 0.003) and subsequent malignancies (P = 0.006). Functional analyses demonstrated that wild-type XAF1 enhances transactivation of wild-type and hypomorphic TP53 variants, whereas XAF1-E134* is markedly attenuated in this activity. We propose that cosegregation of XAF1-E134* and TP53-R337H mutations leads to a more aggressive cancer phenotype than TP53-R337H alone, with implications for genetic counseling and clinical management of hypomorphic TP53 mutant carriers.Fil: Pinto, Emilia M.. St. Jude Children's Research Hospital; Estados UnidosFil: Figueiredo, Bonald C.. Instituto de Pesquisa Pelé Pequeno Principe; BrasilFil: Chen, Wenan. St. Jude Children's Research Hospital; Estados UnidosFil: Galvao, Henrique C.R.. Hospital de Câncer de Barretos; BrasilFil: Formiga, Maria Nirvana. A.c.camargo Cancer Center; BrasilFil: Fragoso, Maria Candida B.V.. Universidade de Sao Paulo; BrasilFil: Ashton Prolla, Patricia. Universidade Federal do Rio Grande do Sul; BrasilFil: Ribeiro, Enilze M.S.F.. Universidade Federal do Paraná; BrasilFil: Felix, Gabriela. Universidade Federal da Bahia; BrasilFil: Costa, Tatiana E.B.. Hospital Infantil Joana de Gusmao; BrasilFil: Savage, Sharon A.. National Cancer Institute; Estados UnidosFil: Yeager, Meredith. National Cancer Institute; Estados UnidosFil: Palmero, Edenir I.. Hospital de Câncer de Barretos; BrasilFil: Volc, Sahlua. Hospital de Câncer de Barretos; BrasilFil: Salvador, Hector. Hospital Sant Joan de Deu Barcelona; EspañaFil: Fuster Soler, Jose Luis. Hospital Clínico Universitario Virgen de la Arrixaca; EspañaFil: Lavarino, Cinzia. Hospital Sant Joan de Deu Barcelona; EspañaFil: Chantada, Guillermo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. St. Jude Children's Research Hospital; Estados UnidosFil: Vaur, Dominique. Comprehensive Cancer Center François Baclesse; FranciaFil: Odone Filho, Vicente. Universidade de Sao Paulo; BrasilFil: Brugières, Laurence. Institut de Cancerologie Gustave Roussy; FranciaFil: Else, Tobias. University of Michigan; Estados UnidosFil: Stoffel, Elena M.. University of Michigan; Estados UnidosFil: Maxwell, Kara N.. University of Pennsylvania; Estados UnidosFil: Achatz, Maria Isabel. Hospital Sirio-libanês; BrasilFil: Kowalski, Luis. A.c.camargo Cancer Center; BrasilFil: De Andrade, Kelvin C.. National Cancer Institute; Estados UnidosFil: Pappo, Alberto. St. Jude Children's Research Hospital; Estados UnidosFil: Letouze, Eric. Centre de Recherche Des Cordeliers; FranciaFil: Latronico, Ana Claudia. Universidade de Sao Paulo; BrasilFil: Mendonca, Berenice B.. Universidade de Sao Paulo; BrasilFil: Almeida, Madson Q.. Universidade de Sao Paulo; BrasilFil: Brondani, Vania B.. Universidade de Sao Paulo; BrasilFil: Bittar, Camila M.. Universidade Federal do Rio Grande do Sul; BrasilFil: Soares, Emerson W.S.. Hospital Do Câncer de Cascavel; BrasilFil: Mathias, Carolina. Universidade Federal do Paraná; BrasilFil: Ramos, Cintia R.N.. Hospital de Câncer de Barretos; BrasilFil: Machado, Moara. National Cancer Institute; Estados UnidosFil: Zhou, Weiyin. National Cancer Institute; Estados UnidosFil: Jones, Kristine. National Cancer Institute; Estados UnidosFil: Vogt, Aurelie. National Cancer Institute; Estados UnidosFil: Klincha, Payal P.. National Cancer Institute; Estados UnidosFil: Santiago, Karina M.. A.c.camargo Cancer Center; BrasilFil: Komechen, Heloisa. Instituto de Pesquisa Pelé Pequeno Principe; BrasilFil: Paraizo, Mariana M.. Instituto de Pesquisa Pelé Pequeno Principe; BrasilFil: Parise, Ivy Z.S.. Instituto de Pesquisa Pelé Pequeno Principe; BrasilFil: Hamilton, Kayla V.. St. Jude Children's Research Hospital; Estados UnidosFil: Wang, Jinling. St. Jude Children's Research Hospital; Estados UnidosFil: Rampersaud, Evadnie. St. Jude Children's Research Hospital; Estados UnidosFil: Clay, Michael R.. St. Jude Children's Research Hospital; Estados UnidosFil: Murphy, Andrew J.. St. Jude Children's Research Hospital; Estados UnidosFil: Lalli, Enzo. Institut de Pharmacologie Moléculaire et Cellulaire; FranciaFil: Nichols, Kim E.. St. Jude Children's Research Hospital; Estados UnidosFil: Ribeiro, Raul C.. St. Jude Children's Research Hospital; Estados UnidosFil: Rodriguez-Galindo, Carlos. St. Jude Children's Research Hospital; Estados UnidosFil: Korbonits, Marta. Queen Mary University of London; Reino UnidoFil: Zhang, Jinghui. St. Jude Children's Research Hospital; Estados UnidosFil: Thomas, Mark G.. Colegio Universitario de Londres; Reino UnidoFil: Connelly, Jon P.. St. Jude Children's Research Hospital; Estados UnidosFil: Pruett-Miller, Shondra. St. Jude Children's Research Hospital; Estados UnidosFil: Diekmann, Yoan. Colegio Universitario de Londres; Reino UnidoFil: Neale, Geoffrey. St. Jude Children's Research Hospital; Estados UnidosFil: Wu, Gang. St. Jude Children's Research Hospital; Estados UnidosFil: Zambetti, Gerard P.. St. Jude Children's Research Hospital; Estados Unido

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Preventive medicine of von Hippel-Lindau disease-associated pancreatic neuroendocrine tumors

Pancreatic neuroendocrine tumors (PanNETs) are rare in von Hippel-Lindau disease (VHL) but cause serious morbidity and mortality. Management guidelines for VHL-PanNETs continue to be based on limited evidence, and survival data to guide surgical management are lacking. We established the European-American-Asian-VHL-PanNET-Registry to assess data for risks for metastases, survival and long-term outcomes to provide best management recommendations. Of 2330 VHL patients, 273 had a total of 484 PanNETs. Median age at diagnosis of PanNET was 35 years (range 10-75). Fifty-five (20%) patients had metastatic PanNETs. Metastatic PanNETs were significantly larger (median size 5 vs 2\u2009cm; P\u20091.5\u2009cm in diameter were operated. Ten-year survival was significantly longer in operated vs non-operated patients, in particular for PanNETs <2.8\u2009cm vs 652.8\u2009cm (94% vs 85% by 10 years; P\u2009=\u20090.020; 80% vs 50% at 10 years; P\u2009=\u20090.030). This study demonstrates that patients with PanNET approaching the cut-off diameter of 2.8\u2009cm should be operated. Mutations in exon 3, especially of codons 161/167 are at enhanced risk for metastatic PanNETs. Survival is significantly longer in operated non-metastatic VHL-PanNETs

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

An Improved Three-phase Amb Distribution System State Estimator

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)State estimators (SEs) are required to enable the evolving and increasingly important role of communications and control in smart distribution systems. In this context, this paper presents an improved three-phase admittance matrix-based (AMB) SE for medium voltage systems to tackle issues related to zero injections, consistency, and the inclusion of voltage measurements. Here, the state variables are the real and imaginary parts of the complex bus voltages, while power and voltage measurements are converted into equivalent currents and voltages, respectively. The key improvements include: 1) considering zero injections through a linear nonweighted procedure, 2) using phasor rotation for calculation of the equivalent voltage measurements, and 3) including the covariance between real and imaginary parts of equivalent current measurements. Despite these new characteristics, the proposed improved AMB SE (ISE) features constant coefficient matrices, thus resulting in reduced computational times. The performance of the ISE is assessed considering a real UK medium voltage system. Its consistency is assessed via a Monte Carlo analysis. Comparisons with other AMB SEs demonstrate that the proposed three-phase ISE is more robust, statistically more consistent, and computationally very competitive.32214631473Sao Paulo Research FoundationConselho Nacional de Desenvolvimento Cientifico e TecnologicoCoordenacao de Aperfeicoamento de Pessoal de Nivel SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES